EPSA paper: Inference for multilevel models when the number of clusters is small.

21 June 2014

In a recent article published in AJPS it is claimed that Bayesian
estimators have a superior performance in the estimation of the
influence of group-level covariates, especially if the number of
groups/clusters is small. In the paper presented at
EPSA, we show that the problems addressed by
Bayesian techniques can also be adequately addressed by a frequentist
technique, restricted maximum likelihood, without the problems
involved in Bayesian estimation, such as the computational cost and the
need to select an appropriate prior.

Coverage error of Student-t based confidence intervals of the
coefficient of a group-level covariate, with different numbers of groups
and two different estimation methods, maximum likelihood and
restricted maximum likelihood.¶